{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7581e6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Tax Revenue Data:\n",
      "         Year  Tax_Revenue\n",
      "0  2020-01-01    -0.331278\n",
      "1  2020-01-02    -0.680853\n",
      "2  2020-01-03     5.321305\n",
      "3  2020-01-04     4.480989\n",
      "4  2020-01-05     4.623289\n",
      "..        ...          ...\n",
      "95 2020-04-05     5.885672\n",
      "96 2020-04-06     6.767590\n",
      "97 2020-04-07    10.421247\n",
      "98 2020-04-08     7.767503\n",
      "99 2020-04-09     9.273891\n",
      "\n",
      "[100 rows x 2 columns]\n",
      "\n",
      "Data series length: 100\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['DejaVu Sans']  \n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "years = pd.date_range(start='2020-01-01', periods=100, freq='D')\n",
    "trend = np.linspace(0, 10, 100)\n",
    "seasonality = 5 * np.sin(np.linspace(0, 10*np.pi, 100))\n",
    "noise = np.random.randn(100) * 1.5\n",
    "tax_revenue = trend + seasonality + noise\n",
    "\n",
    "data = pd.DataFrame({\n",
    "    'Year': years,\n",
    "    'Tax_Revenue': tax_revenue\n",
    "})\n",
    "\n",
    "print(\"Original Tax Revenue Data:\")\n",
    "print(data)\n",
    "print(f\"\\nData series length: {len(tax_revenue)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "627c02bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daniel Stationarity Test Results:\n",
      "Spearman rank correlation qs = 0.5596\n",
      "Test statistic T = 6.6840\n",
      "Critical value t_0.025(98) = 1.9845\n",
      "|T| = 6.6840 > 1.9845\n",
      "Reject H0, series is non-stationary with upward trend\n",
      "\n",
      "First-order difference series:\n",
      "b_t = a_t - a_(t-1)\n",
      "Difference series: [-0.3495748   6.00215858 -0.8403162   0.1423003   2.70445304 -4.51385915\n",
      "  1.40905871 -0.82269363  0.76751415 -0.55398206 -2.01157387 -3.11849022\n",
      " -0.51799207 -4.76411353  1.24439489  1.82561716  1.61795656 -0.49128453\n",
      "  0.37798759  4.28202008  3.22818895 -1.15111412  1.64710061  3.04185114\n",
      " -3.96503619  1.95806877 -1.10639883 -0.0834874  -1.7235273  -2.08476579\n",
      "  0.74022193 -4.04628561 -1.53909787  3.09946454 -1.06554521 -0.15766249\n",
      "  0.97974157  3.63483779  0.77516119  0.93334222 -0.82844767  4.64342131\n",
      " -0.20404174  0.1590694   1.96515809  0.20269762 -4.82814278  1.51802602\n",
      "  1.96802966 -4.8193361  -1.8468262  -0.56124735 -2.25751152 -1.22951798\n",
      "  1.25806524  0.751096    1.39075766  4.26067203 -3.3739869   3.17571149\n",
      "  0.8396255   2.03943717  1.22327384 -0.22479697  1.47351108 -2.07476494\n",
      " -0.55326484  1.34292749 -4.47818925 -0.72744678 -0.4445141  -1.56701831\n",
      " -0.89858323 -0.46480832  1.58420417 -0.97088055  1.69051366  0.80702168\n",
      "  2.03072902  4.71559751 -0.6670441   3.32818912 -0.77469106 -1.41282451\n",
      "  4.34598829 -6.9589654   3.16787758 -1.66821635 -0.87104087 -1.28101336\n",
      " -3.85175938  2.15621939 -3.83497669  3.60410923 -1.25576096  0.88191813\n",
      "  3.65365765 -2.65374421  1.50638827]\n",
      "\n",
      "Stationarity test for difference series:\n",
      "Daniel Stationarity Test Results:\n",
      "Spearman rank correlation qs = 0.0023\n",
      "Test statistic T = 0.0225\n",
      "Critical value t_0.025(97) = 1.9847\n",
      "|T| = 0.0225 <= 1.9847\n",
      "Accept H0, series is stationary\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 2. 平稳性检验（Daniel检验/Spearman秩相关系数）\n",
    "def daniel_test(series, alpha=0.05):\n",
    "    \"\"\"\n",
    "    Daniel检验序列平稳性\n",
    "    H0: 序列平稳\n",
    "    H1: 序列非平稳（存在趋势）\n",
    "    \"\"\"\n",
    "    n = len(series)\n",
    "    # 计算秩\n",
    "    ranks = stats.rankdata(series)\n",
    "    # 计算时间序列的秩\n",
    "    time_ranks = np.arange(1, n+1)\n",
    "    \n",
    "    # 计算Spearman秩相关系数\n",
    "    qs = stats.spearmanr(time_ranks, ranks)[0]\n",
    "    \n",
    "    # 避免除零错误\n",
    "    if abs(qs) >= 0.9999:\n",
    "        qs = 0.9999 * np.sign(qs)\n",
    "    \n",
    "    # 计算检验统计量\n",
    "    T = qs * np.sqrt(n - 2) / np.sqrt(1 - qs**2)\n",
    "    \n",
    "    # 临界值(ppf 返回分布的第 q 分位数) 此处做双侧检验\n",
    "    t_critical = stats.t.ppf(1 - alpha/2, n - 2)\n",
    "    \n",
    "    print(f\"Daniel Stationarity Test Results:\")\n",
    "    print(f\"Spearman rank correlation qs = {qs:.4f}\")\n",
    "    print(f\"Test statistic T = {T:.4f}\")\n",
    "    print(f\"Critical value t_{alpha/2}({n-2}) = {t_critical:.4f}\")\n",
    "    print(f\"|T| = {abs(T):.4f} {'>' if abs(T) > t_critical else '<='} {t_critical:.4f}\")\n",
    "    \n",
    "    if abs(T) > t_critical:\n",
    "        # 拒绝 H_0, 认为序列非平稳； qs > 0 则有上升趋势; 反之有下降趋势\n",
    "        trend = \"upward trend\" if qs > 0 else \"downward trend\"\n",
    "        print(f\"Reject H0, series is non-stationary with {trend}\")\n",
    "        return False, trend\n",
    "    else:\n",
    "        print(\"Accept H0, series is stationary\")\n",
    "        return True, \"no significant trend\"\n",
    "\n",
    "# 检验原始序列平稳性\n",
    "is_stationary, trend = daniel_test(tax_revenue)\n",
    "\n",
    "# 3. 一阶差分处理\n",
    "diff_series = np.diff(tax_revenue)\n",
    "print(f\"\\nFirst-order difference series:\")\n",
    "print(f\"b_t = a_t - a_(t-1)\")\n",
    "print(f\"Difference series: {diff_series}\")\n",
    "\n",
    "# 检验差分序列平稳性\n",
    "print(f\"\\nStationarity test for difference series:\")\n",
    "is_diff_stationary, diff_trend = daniel_test(diff_series)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e6aadf17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.3495748   6.00215858 -0.8403162   0.1423003   2.70445304 -4.51385915\n",
      "  1.40905871 -0.82269363  0.76751415 -0.55398206 -2.01157387 -3.11849022\n",
      " -0.51799207 -4.76411353  1.24439489  1.82561716  1.61795656 -0.49128453\n",
      "  0.37798759  4.28202008  3.22818895 -1.15111412  1.64710061  3.04185114\n",
      " -3.96503619  1.95806877 -1.10639883 -0.0834874  -1.7235273  -2.08476579\n",
      "  0.74022193 -4.04628561 -1.53909787  3.09946454 -1.06554521 -0.15766249\n",
      "  0.97974157  3.63483779  0.77516119  0.93334222 -0.82844767  4.64342131\n",
      " -0.20404174  0.1590694   1.96515809  0.20269762 -4.82814278  1.51802602\n",
      "  1.96802966 -4.8193361  -1.8468262  -0.56124735 -2.25751152 -1.22951798\n",
      "  1.25806524  0.751096    1.39075766  4.26067203 -3.3739869   3.17571149\n",
      "  0.8396255   2.03943717  1.22327384 -0.22479697  1.47351108 -2.07476494\n",
      " -0.55326484  1.34292749 -4.47818925 -0.72744678 -0.4445141  -1.56701831\n",
      " -0.89858323 -0.46480832  1.58420417 -0.97088055  1.69051366  0.80702168\n",
      "  2.03072902  4.71559751 -0.6670441   3.32818912 -0.77469106 -1.41282451\n",
      "  4.34598829 -6.9589654   3.16787758 -1.66821635 -0.87104087 -1.28101336\n",
      " -3.85175938  2.15621939 -3.83497669  3.60410923 -1.25576096  0.88191813\n",
      "  3.65365765 -2.65374421  1.50638827]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.3495748   6.00215858 -0.8403162   0.1423003   2.70445304 -4.51385915\n",
      "  1.40905871 -0.82269363  0.76751415 -0.55398206 -2.01157387 -3.11849022\n",
      " -0.51799207 -4.76411353  1.24439489  1.82561716  1.61795656 -0.49128453\n",
      "  0.37798759  4.28202008  3.22818895 -1.15111412  1.64710061  3.04185114\n",
      " -3.96503619  1.95806877 -1.10639883 -0.0834874  -1.7235273  -2.08476579\n",
      "  0.74022193 -4.04628561 -1.53909787  3.09946454 -1.06554521 -0.15766249\n",
      "  0.97974157  3.63483779  0.77516119  0.93334222 -0.82844767  4.64342131\n",
      " -0.20404174  0.1590694   1.96515809  0.20269762 -4.82814278  1.51802602\n",
      "  1.96802966 -4.8193361  -1.8468262  -0.56124735 -2.25751152 -1.22951798\n",
      "  1.25806524  0.751096    1.39075766  4.26067203 -3.3739869   3.17571149\n",
      "  0.8396255   2.03943717  1.22327384 -0.22479697  1.47351108 -2.07476494\n",
      " -0.55326484  1.34292749 -4.47818925 -0.72744678 -0.4445141  -1.56701831\n",
      " -0.89858323 -0.46480832  1.58420417 -0.97088055  1.69051366  0.80702168\n",
      "  2.03072902  4.71559751 -0.6670441   3.32818912 -0.77469106 -1.41282451\n",
      "  4.34598829 -6.9589654   3.16787758 -1.66821635 -0.87104087 -1.28101336\n",
      " -3.85175938  2.15621939 -3.83497669  3.60410923 -1.25576096  0.88191813\n",
      "  3.65365765 -2.65374421  1.50638827]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbb23c31900>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.3495748   6.00215858 -0.8403162   0.1423003   2.70445304 -4.51385915\n",
      "  1.40905871 -0.82269363  0.76751415 -0.55398206 -2.01157387 -3.11849022\n",
      " -0.51799207 -4.76411353  1.24439489  1.82561716  1.61795656 -0.49128453\n",
      "  0.37798759  4.28202008  3.22818895 -1.15111412  1.64710061  3.04185114\n",
      " -3.96503619  1.95806877 -1.10639883 -0.0834874  -1.7235273  -2.08476579\n",
      "  0.74022193 -4.04628561 -1.53909787  3.09946454 -1.06554521 -0.15766249\n",
      "  0.97974157  3.63483779  0.77516119  0.93334222 -0.82844767  4.64342131\n",
      " -0.20404174  0.1590694   1.96515809  0.20269762 -4.82814278  1.51802602\n",
      "  1.96802966 -4.8193361  -1.8468262  -0.56124735 -2.25751152 -1.22951798\n",
      "  1.25806524  0.751096    1.39075766  4.26067203 -3.3739869   3.17571149\n",
      "  0.8396255   2.03943717  1.22327384 -0.22479697  1.47351108 -2.07476494\n",
      " -0.55326484  1.34292749 -4.47818925 -0.72744678 -0.4445141  -1.56701831\n",
      " -0.89858323 -0.46480832  1.58420417 -0.97088055  1.69051366  0.80702168\n",
      "  2.03072902  4.71559751 -0.6670441   3.32818912 -0.77469106 -1.41282451\n",
      "  4.34598829 -6.9589654   3.16787758 -1.66821635 -0.87104087 -1.28101336\n",
      " -3.85175938  2.15621939 -3.83497669  3.60410923 -1.25576096  0.88191813\n",
      "  3.65365765 -2.65374421  1.50638827]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbb23c31900>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print(diff_series)\n",
    "plt.plot(diff_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "30e39e72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -6.853313867600016\n",
      "p-value: 1.6727279817620099e-09\n",
      "Critical Values:\n",
      "\t1%: -3.509\n",
      "\t5%: -2.896\n",
      "\t10%: -2.585\n"
     ]
    }
   ],
   "source": [
    "adf_test = adfuller(diff_series)\n",
    "print(\"ADF Statistic:\", adf_test[0])\n",
    "print(\"p-value:\", adf_test[1])\n",
    "print(\"Critical Values:\")\n",
    "for key, value in adf_test[4].items():\n",
    "    print('\\t%s: %.3f' % (key, value))\n",
    "\n",
    "# print(\"ADF统计量大于所有三个显著性水平下的临界值，不能拒绝存在单位根的假设\")\n",
    "# print(\"P 值也远大于 0.05\")\n",
    "# print(\"所以这个序列在当前形式下是非平稳的\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8111acf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ljung-Box test statistic: 17.005681405540695\n",
      "p-value: 0.004489034631239292\n"
     ]
    }
   ],
   "source": [
    "# diff series 仍然不是平稳的，为了后面的分析，假设是平稳的\n",
    "# 再做白噪声检验\n",
    "test_ljung_box = sm.stats.diagnostic.acorr_ljungbox(diff_series, lags=[i for i in range(5, 13, 5)])\n",
    "print(\"Ljung-Box test statistic:\", test_ljung_box.iloc[0, 0])\n",
    "print(\"p-value:\", test_ljung_box.iloc[0, 1])\n",
    "\n",
    "# p 值极大，则说明原序列是白噪声"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2279a5a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# 模型定阶\n",
    "# d = 1 已知\n",
    "fig, ax = plt.subplots(2, 1, figsize=(12, 8))\n",
    "\n",
    "# 截尾比拖尾趋近于0更加迅速\n",
    "# 截尾在后期不会再有明显的增加。\n",
    "\n",
    "sm.graphics.tsa.plot_acf(diff_series, lags=10, ax=ax[0])\n",
    "\n",
    "sm.graphics.tsa.plot_pacf(diff_series, lags=10, ax=ax[1])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# 下图所示，在 lag=1 处有明显负相关，lag=2 稍微正相关，之后逐渐往 0 附近衰减。\n",
    "\n",
    "# 没有在某个固定阶数后直接“消失”，而是慢慢减小。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d7cd311",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                  100\n",
      "Model:                 ARIMA(1, 1, 0)   Log Likelihood                -227.099\n",
      "Date:                Tue, 26 Aug 2025   AIC                            458.199\n",
      "Time:                        17:02:56   BIC                            463.389\n",
      "Sample:                             0   HQIC                           460.299\n",
      "                                - 100                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1         -0.2342      0.108     -2.159      0.031      -0.447      -0.022\n",
      "sigma2         5.7513      0.860      6.684      0.000       4.065       7.438\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):                 0.21\n",
      "Prob(Q):                              0.83   Prob(JB):                         0.90\n",
      "Heteroskedasticity (H):               0.95   Skew:                            -0.08\n",
      "Prob(H) (two-sided):                  0.89   Kurtosis:                         2.85\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# p=2, d=1, q=1\n",
    "model = ARIMA(tax_revenue, order=(1, 1, 0))\n",
    "results = model.fit()\n",
    "\n",
    "# 预测阶段\n",
    "# 如果 ​​95% CI 不包含 0​​ → p 值通常 < 0.05（系数显著）。\n",
    "# 且越窄越精确\n",
    "# 根据 p 值, AIC, BIC 来调整参数\n",
    "print(results.summary())\n",
    "\n",
    "forecast_steps = 20\n",
    "forecast = results.get_forecast(steps=forecast_steps)\n",
    "forecast_mean = forecast.predicted_mean\n",
    "forecast_ci = forecast.conf_int() # 95% 置信区间\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(data['Year'], data['Tax_Revenue'], label='Observed')\n",
    "\n",
    "# Get forecast dates\n",
    "forecast_dates = pd.date_range(data['Year'].iloc[-1] + pd.Timedelta(days=1), periods=forecast_steps, freq='D')\n",
    "\n",
    "plt.plot(forecast_dates, forecast_mean, label='Forecast')\n",
    "plt.fill_between(forecast_dates, forecast_ci[:, 0], forecast_ci[:, 1], color='pink', alpha=0.3)\n",
    "plt.title('ARIMA prediction:')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# 可以看到越到后面，预测越不可靠（置信区间变大）"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "math_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
